Pub Date : 2008-07-21DOI: 10.1109/SAM.2008.4606878
C. Lam, A. Singer
Traditional beamforming algorithms require perfect knowledge of the source direction-of-arrival (DOA) to generate beamformer weights that yield high signal-to-interference-plus-noise ratio (SINR). We apply a Bayesian approach to adaptive beamforming such that the algorithm automatically tunes to the underlying DOA that is not known a priori to the user. The proposed beamformer can be viewed as a weighted mixture of minimum variance distortionless response (MVDR) beamformers combined according to the data-driven posterior probability density function (PDF) of the DOA. Previous studies use discrete samples to capture the spatial variation of the posterior PDF. In this work, we show that, in case of uniform linear array (ULA), the posterior PDF can be represented as a product of the prior PDF and a number of von Mises PDFpsilas, each approximated by the frequency response of a recursive filter. The beamformer weights can then be computed from the corresponding recursive filtering operations. This leads to an algorithm that preserves the continuity of the parameter space and is capable to resolve any amount of DOA error.
{"title":"A recursive filter approach to adaptive Bayesian beamforming for unknown DOA","authors":"C. Lam, A. Singer","doi":"10.1109/SAM.2008.4606878","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606878","url":null,"abstract":"Traditional beamforming algorithms require perfect knowledge of the source direction-of-arrival (DOA) to generate beamformer weights that yield high signal-to-interference-plus-noise ratio (SINR). We apply a Bayesian approach to adaptive beamforming such that the algorithm automatically tunes to the underlying DOA that is not known a priori to the user. The proposed beamformer can be viewed as a weighted mixture of minimum variance distortionless response (MVDR) beamformers combined according to the data-driven posterior probability density function (PDF) of the DOA. Previous studies use discrete samples to capture the spatial variation of the posterior PDF. In this work, we show that, in case of uniform linear array (ULA), the posterior PDF can be represented as a product of the prior PDF and a number of von Mises PDFpsilas, each approximated by the frequency response of a recursive filter. The beamformer weights can then be computed from the corresponding recursive filtering operations. This leads to an algorithm that preserves the continuity of the parameter space and is capable to resolve any amount of DOA error.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130248427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-07-21DOI: 10.1109/SAM.2008.4606918
F. Roemer, M. Haardt
In this paper we study the R-way Parallel Factor Analysis (also referred to as R-way PARAFAC) problem. This branch of multi-way signal processing has received increased attention recently which is due to the versatility of the model as well as the identifiability results demonstrating its superiority to matrix-only (2-way) approaches. In R-way PARAFAC analysis, the goal is to decompose an R-dimensional tensor into a minimal sum of rank-1 terms. So far, there exist sub-optimal closed-form solutions as well as iterative techniques for finding these decompositions. However, the latter often require many iterations to converge. In this contribution we demonstrate that the R-way PARAFAC decomposition can be reduced to a set of simultaneous matrix diagonalization problems. Exploiting the structure of the R-dimensional problem, we obtain several estimates for each of the factors and present a "best matching" scheme to select the best estimate for each factor. By means of computer simulations we compare our closed-form solution to an iterative technique and demonstrate the enhanced robustness in critical scenarios.
{"title":"A closed-form solution for multilinear PARAFAC decompositions","authors":"F. Roemer, M. Haardt","doi":"10.1109/SAM.2008.4606918","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606918","url":null,"abstract":"In this paper we study the R-way Parallel Factor Analysis (also referred to as R-way PARAFAC) problem. This branch of multi-way signal processing has received increased attention recently which is due to the versatility of the model as well as the identifiability results demonstrating its superiority to matrix-only (2-way) approaches. In R-way PARAFAC analysis, the goal is to decompose an R-dimensional tensor into a minimal sum of rank-1 terms. So far, there exist sub-optimal closed-form solutions as well as iterative techniques for finding these decompositions. However, the latter often require many iterations to converge. In this contribution we demonstrate that the R-way PARAFAC decomposition can be reduced to a set of simultaneous matrix diagonalization problems. Exploiting the structure of the R-dimensional problem, we obtain several estimates for each of the factors and present a \"best matching\" scheme to select the best estimate for each factor. By means of computer simulations we compare our closed-form solution to an iterative technique and demonstrate the enhanced robustness in critical scenarios.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131300483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-07-21DOI: 10.1109/SAM.2008.4606917
T. Mei, A. Mertins, F. Yin
Many convolutive blind source separation (BSS) approaches are generalized from instantaneous BSS methods in either time or frequency domain. In this paper, we establish in a general way the inner relationship between the time-domain instantaneous BSS and the frequency-domain convolutive BSS. From this point of view, the time-domain approaches for instantaneous mixture separation are generalized to those for convolutive mixture separation in the frequency domain. Two examples are given to illustrate the feasibility of the proposed approach.
{"title":"On the generalization of blind source separation algorithms from instantaneous to convolutive mixtures","authors":"T. Mei, A. Mertins, F. Yin","doi":"10.1109/SAM.2008.4606917","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606917","url":null,"abstract":"Many convolutive blind source separation (BSS) approaches are generalized from instantaneous BSS methods in either time or frequency domain. In this paper, we establish in a general way the inner relationship between the time-domain instantaneous BSS and the frequency-domain convolutive BSS. From this point of view, the time-domain approaches for instantaneous mixture separation are generalized to those for convolutive mixture separation in the frequency domain. Two examples are given to illustrate the feasibility of the proposed approach.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126400793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-07-21DOI: 10.1109/SAM.2008.4606907
S. Max, P. Gulden, M. Vossiek
In this paper, we introduce the novel synthetic aperture secondary (SAS) radar positioning technique for the localization of a vehicle. The SAS technique is based on a frequency-modulated continuous wave (FMCW) secondary radar concept where the interrogating radar signal is reflected coherently by a backscatter transponder. It will be shown that SAS positioning is a very efficient way to combine the data of wireless positioning systems with the data from assisting sensors. Our novel SAS local positioning technique outperforms the usual integrated or hybrid navigation approaches based on multilateration notably. A 5.8-GHz wireless local positioning system has been built to test our SAS concept. The synthetic aperture is set up by a moving vehicle. Aperture points are determined with the use of gyroscope and tachymeter sensor data. A newly developed SAS reconstruction algorithm estimates the most likely transponder positions. Based on these estimations, the vehicle determines its position with an accuracy of approximately 5 cm even in complex multipath environments.
{"title":"Localization of backscatter transponders based on a synthetic aperture secondary radar imaging approach","authors":"S. Max, P. Gulden, M. Vossiek","doi":"10.1109/SAM.2008.4606907","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606907","url":null,"abstract":"In this paper, we introduce the novel synthetic aperture secondary (SAS) radar positioning technique for the localization of a vehicle. The SAS technique is based on a frequency-modulated continuous wave (FMCW) secondary radar concept where the interrogating radar signal is reflected coherently by a backscatter transponder. It will be shown that SAS positioning is a very efficient way to combine the data of wireless positioning systems with the data from assisting sensors. Our novel SAS local positioning technique outperforms the usual integrated or hybrid navigation approaches based on multilateration notably. A 5.8-GHz wireless local positioning system has been built to test our SAS concept. The synthetic aperture is set up by a moving vehicle. Aperture points are determined with the use of gyroscope and tachymeter sensor data. A newly developed SAS reconstruction algorithm estimates the most likely transponder positions. Based on these estimations, the vehicle determines its position with an accuracy of approximately 5 cm even in complex multipath environments.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121304112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-07-21DOI: 10.1109/SAM.2008.4606858
R. Sood, Hong Xiao
We introduce a method for the synthesis of pulse pairs that satisfy simultaneously the time-bandwidth condition and the maximum cross-correlation condition for all lags. Pulse pairs with such properties are essential to synthetic aperture imaging - including both SAS and SAR - when multiple transmitters are employed. Formulating the problem in a finite dimensional Euclidean space setting, we identify the geometric properties of the feasible set that arises from the maximum cross-correlation condition. We then develop an iterative algorithm that modifies the feasible set such that additional unit energy constraints on the signal pair are also satisfied. We illustrate the effectiveness of the method with several examples, where the cross-correlation function shows significant improvements over both the PN sequences, and pulses obtained by a previously reported method.
{"title":"Geometric construction of pulse pairs with small cross-correlation for dual transmitter synthetic aperture imaging","authors":"R. Sood, Hong Xiao","doi":"10.1109/SAM.2008.4606858","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606858","url":null,"abstract":"We introduce a method for the synthesis of pulse pairs that satisfy simultaneously the time-bandwidth condition and the maximum cross-correlation condition for all lags. Pulse pairs with such properties are essential to synthetic aperture imaging - including both SAS and SAR - when multiple transmitters are employed. Formulating the problem in a finite dimensional Euclidean space setting, we identify the geometric properties of the feasible set that arises from the maximum cross-correlation condition. We then develop an iterative algorithm that modifies the feasible set such that additional unit energy constraints on the signal pair are also satisfied. We illustrate the effectiveness of the method with several examples, where the cross-correlation function shows significant improvements over both the PN sequences, and pulses obtained by a previously reported method.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121501568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-07-21DOI: 10.1109/SAM.2008.4606920
C. De Luigi, E. Moreau
In this paper, we address the problem of blind source separation of non circular digital communication signals. An optimal combination of statistics obtained from fourth order cumulants that achieves the separation of non-circular sources is proposed.
{"title":"Optimal combination of fourth order statistics for non-circular source separation","authors":"C. De Luigi, E. Moreau","doi":"10.1109/SAM.2008.4606920","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606920","url":null,"abstract":"In this paper, we address the problem of blind source separation of non circular digital communication signals. An optimal combination of statistics obtained from fourth order cumulants that achieves the separation of non-circular sources is proposed.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114186421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-07-21DOI: 10.1109/SAM.2008.4606863
K. Bell, R. Pitre
The maximum a posteriori penalty function (MAP-PF) approach is applied to three-dimensional (3D) target position tracking of multiple wideband sources using multiple distributed sensor arrays. The track estimation problem is formulated directly from the array data using the maximum a posteriori (MAP) estimation criterion. The penalty function (PF) method of nonlinear programming is used to obtain a tractable solution. A sequential update procedure is developed in which penalized maximum likelihood estimates of target directions-of-arrival (DOAs) and spectra are computed at each array and then used as synthetic measurements in a set of extended Kalman filters. The two steps are coupled via the penalty function. The current target states are used to guide the DOA/spectrum estimation, and the estimated signal spectra control the influence of the DOA estimates from each array on the final track estimates. The algorithm can be implemented in a decentralized manner where DOA/spectrum estimation is performed at the arrays, and track estimation is performed at a central processing site.
{"title":"MAP-PF 3D position tracking using multiple sensor array","authors":"K. Bell, R. Pitre","doi":"10.1109/SAM.2008.4606863","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606863","url":null,"abstract":"The maximum a posteriori penalty function (MAP-PF) approach is applied to three-dimensional (3D) target position tracking of multiple wideband sources using multiple distributed sensor arrays. The track estimation problem is formulated directly from the array data using the maximum a posteriori (MAP) estimation criterion. The penalty function (PF) method of nonlinear programming is used to obtain a tractable solution. A sequential update procedure is developed in which penalized maximum likelihood estimates of target directions-of-arrival (DOAs) and spectra are computed at each array and then used as synthetic measurements in a set of extended Kalman filters. The two steps are coupled via the penalty function. The current target states are used to guide the DOA/spectrum estimation, and the estimated signal spectra control the influence of the DOA estimates from each array on the final track estimates. The algorithm can be implemented in a decentralized manner where DOA/spectrum estimation is performed at the arrays, and track estimation is performed at a central processing site.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131542206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-07-21DOI: 10.1109/SAM.2008.4606855
J. Marot, C. Fossati, S. Bourennane
Source localization is based on the spectral matrix algebraic properties. Propagator, and Ermolaev-Gershman (EG) noneigenvector algorithms exhibit a low computational load. Propagator is based on spectral matrix partitioning. EG algorithm obtains an approximation of noise subspace using an adjustable power parameter of the spectral matrix and choosing a threshold value. In this paper, we aim at demonstrating the usefulness of QR and LU factorizations of the spectral matrix to improve these methods. Experiments show that the modified propagator and EG algorithms based on factorized spectral matrix lead to better localization results, compared to the existing methods.
{"title":"Fast subspace-based source localization methods","authors":"J. Marot, C. Fossati, S. Bourennane","doi":"10.1109/SAM.2008.4606855","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606855","url":null,"abstract":"Source localization is based on the spectral matrix algebraic properties. Propagator, and Ermolaev-Gershman (EG) noneigenvector algorithms exhibit a low computational load. Propagator is based on spectral matrix partitioning. EG algorithm obtains an approximation of noise subspace using an adjustable power parameter of the spectral matrix and choosing a threshold value. In this paper, we aim at demonstrating the usefulness of QR and LU factorizations of the spectral matrix to improve these methods. Experiments show that the modified propagator and EG algorithms based on factorized spectral matrix lead to better localization results, compared to the existing methods.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132913634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-07-21DOI: 10.1109/SAM.2008.4606868
D. Bielefeld, G. Fabeck, R. Mathar
The interplay between signal processing and wireless networking plays a crucial role in sensor networks deployed for detection and estimation applications. In this paper, an opportunistic power assignment strategy for IR-UWB sensor networks is presented which is designed to optimize detection performance in terms of the global probability of error. The opportunistic power assignment strategy utilizes both the detection error probabilities of individual sensors as well as network topology information, leading to significant performance gains compared to uniform power assignment.
{"title":"Power-aware distributed detection in IR-UWB sensor networks","authors":"D. Bielefeld, G. Fabeck, R. Mathar","doi":"10.1109/SAM.2008.4606868","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606868","url":null,"abstract":"The interplay between signal processing and wireless networking plays a crucial role in sensor networks deployed for detection and estimation applications. In this paper, an opportunistic power assignment strategy for IR-UWB sensor networks is presented which is designed to optimize detection performance in terms of the global probability of error. The opportunistic power assignment strategy utilizes both the detection error probabilities of individual sensors as well as network topology information, leading to significant performance gains compared to uniform power assignment.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114403435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-07-21DOI: 10.1109/SAM.2008.4606811
J. García-Naya, Héctor J. Pérez-Iglesias, A. Dapena, L. Castedo
This paper focuses on blind channel estimation in Alamouti coded systems with one receiving antenna. We present a comparative study of several blind channel estimation techniques, based on high order statistics (HOS) and second order statistics (SOS), in realistic indoor scenarios. These methods are based on eigendecomposition of a square matrix formed with statistics of the observed signals. They also exploit the orthogonality property of the Alamouti coded channel matrix. Experimental evaluation is carried out using a MIMO testbed at the 2.4 GHz band. The results show the excellent performance of the SOS-based blind channel estimation technique in either line of sight (LOS) and Non-LOS (NLOS) indoor scenarios.
{"title":"A comparative study of blind channel identification methods for Alamouti coded systems over indoor transmissions at 2.4 GHz","authors":"J. García-Naya, Héctor J. Pérez-Iglesias, A. Dapena, L. Castedo","doi":"10.1109/SAM.2008.4606811","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606811","url":null,"abstract":"This paper focuses on blind channel estimation in Alamouti coded systems with one receiving antenna. We present a comparative study of several blind channel estimation techniques, based on high order statistics (HOS) and second order statistics (SOS), in realistic indoor scenarios. These methods are based on eigendecomposition of a square matrix formed with statistics of the observed signals. They also exploit the orthogonality property of the Alamouti coded channel matrix. Experimental evaluation is carried out using a MIMO testbed at the 2.4 GHz band. The results show the excellent performance of the SOS-based blind channel estimation technique in either line of sight (LOS) and Non-LOS (NLOS) indoor scenarios.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116035842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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